@inproceedings{huang-etal-2018-modeling,
title = "Modeling Temporality of Human Intentions by Domain Adaptation",
author = "Huang, Xiaolei and
Liu, Lixing and
Carey, Kate and
Woolley, Joshua and
Scherer, Stefan and
Borsari, Brian",
editor = "Riloff, Ellen and
Chiang, David and
Hockenmaier, Julia and
Tsujii, Jun{'}ichi",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
month = oct # "-" # nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-1074",
doi = "10.18653/v1/D18-1074",
pages = "696--701",
abstract = "Categorizing patient{'}s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.",
}
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<abstract>Categorizing patient’s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.</abstract>
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%0 Conference Proceedings
%T Modeling Temporality of Human Intentions by Domain Adaptation
%A Huang, Xiaolei
%A Liu, Lixing
%A Carey, Kate
%A Woolley, Joshua
%A Scherer, Stefan
%A Borsari, Brian
%Y Riloff, Ellen
%Y Chiang, David
%Y Hockenmaier, Julia
%Y Tsujii, Jun’ichi
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
%D 2018
%8 oct nov
%I Association for Computational Linguistics
%C Brussels, Belgium
%F huang-etal-2018-modeling
%X Categorizing patient’s intentions in conversational assessment can help decision making in clinical treatments. Many conversation corpora span broaden a series of time stages. However, it is not clear that how the themes shift in the conversation impact on the performance of human intention categorization (eg., patients might show different behaviors during the beginning versus the end). This paper proposes a method that models the temporal factor by using domain adaptation on clinical dialogue corpora, Motivational Interviewing (MI). We deploy Bi-LSTM and topic model jointly to learn language usage change across different time sessions. We conduct experiments on the MI corpora to show the promising improvement after considering temporality in the classification task.
%R 10.18653/v1/D18-1074
%U https://aclanthology.org/D18-1074
%U https://doi.org/10.18653/v1/D18-1074
%P 696-701
Markdown (Informal)
[Modeling Temporality of Human Intentions by Domain Adaptation](https://aclanthology.org/D18-1074) (Huang et al., EMNLP 2018)
ACL
- Xiaolei Huang, Lixing Liu, Kate Carey, Joshua Woolley, Stefan Scherer, and Brian Borsari. 2018. Modeling Temporality of Human Intentions by Domain Adaptation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 696–701, Brussels, Belgium. Association for Computational Linguistics.